Abstract
High Utility Quantitative Itemset Mining (HUQIM), which is an extension of the original High Utility Itemset Mining (HUIM), has become an important research area that answers the ever-growing need for useful information from the copious pool of data in reality. Due to the nature of the HUQIM problem, its search space is huge and could heavily affect the execution time. Thus, the recently proposed FHUQI-Miner has overcome these limits with novel pruning strategies to narrow the space and outperform previously introduced algorithms. However, there are certain shortcomings that the algorithm still faces. One of the limitations is that the proposed strategies would not operate as efficiently on dense datasets as they would on sparse datasets, resulting from the similarity in structure of the transactions and thus increasing the number of join operations in progress. To address this limitation, this work introduces an enhanced version of the FHUQI-Miner algorithm with an improved TQCS structure to reduce mining time and the number of joins performed.
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Nguyen, L.T.T., Pham, A.N.H., Nguyen, T.D.D., Kozierkiewicz, A., Vo, B., Tung, N.T. (2023). Efficient Pruning Strategy for Mining High Utility Quantitative Itemsets. In: Nguyen, N.T., et al. Advances in Computational Collective Intelligence. ICCCI 2023. Communications in Computer and Information Science, vol 1864. Springer, Cham. https://doi.org/10.1007/978-3-031-41774-0_26
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